scholarly journals A high-quality global gravity field model from CHAMP GPS tracking data and accelerometry (EIGEN-1S)

2002 ◽  
Vol 29 (14) ◽  
pp. 37-1-37-4 ◽  
Author(s):  
Christoph Reigber ◽  
Georges Balmino ◽  
Peter Schwintzer ◽  
Richard Biancale ◽  
Albert Bode ◽  
...  
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Gps Tracking ◽  
Tracking Data ◽  
High Quality ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Global Gravity Field Model

scholarly journals A crustal thickness map of Africa derived from a global gravity field model using Euler deconvolution

2011 ◽  
Vol 187 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Getachew E. Tedla ◽  
M. van der Meijde ◽  
A. A. Nyblade ◽  
F. D. van der Meer
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Crustal Thickness ◽  
Euler Deconvolution ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Global Gravity Field Model

scholarly journals The combined global gravity field model XGM2019e

2020 ◽  
Vol 94 (7) ◽  
Author(s):  
P. Zingerle ◽  
R. Pail ◽  
T. Gruber ◽  
X. Oikonomidou
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Global Gravity Field Model

Fitting the GPS/Leveling Quasi-Geoid Using Bayesian-Regulation BP Neural Network

2011 ◽  
Vol 90-93 ◽  
pp. 2903-2906
Author(s):  
Lei Song ◽  
Xiao Qing Hu
Keyword(s):  
Neural Network ◽  
Gravity Field ◽  
Bp Neural Network ◽  
Field Model ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Fitting In ◽  
Gps Leveling ◽  
Global Gravity Field Model

The 2.5′×2.5′resolution local quasi-geoid is calculated using the global gravity field model and GPS/leveling data of region which points spacing is about 10km with the Bayesian- regulation BP neural network in this paper. The inner and outer precision of quasi-geoid are both superior 0.05m.The result indicat that the Bayesian regulation BP neural network could improve the precision of fitting and restrain the over-fitting in fitting. The region quasi-geoid excelled than 0.05m can be computed using the global gravity field model and about 10km baseline GPS/leveling data in smoothness region.

scholarly journals GOCO06s – A satellite-only global gravity field model

2020 ◽  
Author(s):  
Andreas Kvas ◽  
Jan Martin Brockmann ◽  
Sandro Krauss ◽  
Till Schubert ◽  
Thomas Gruber ◽  
...  
Keyword(s):  
Gravity Field ◽  
Covariance Matrix ◽  
Field Model ◽  
Data Set ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Gravity Observation ◽  
Primary Model ◽  
Global Gravity Field Model ◽  
Variance Covariance Matrix

Abstract. GOCO06s is the latest satellite-only global gravity field model computed by the GOCO (Gravity Observation Combination) project. It is based on over a billion observations acquired over 15 years from 19 satellites with different complementary observation principles. This combination of different measurement techniques is key in providing consistent high-accuracy and best possible spatial resolution of the Earth's gravity field. The motivation for the new release was in the availability of reprocessed observation data for GRACE and GOCE, updated background models, and substantial improvements in the processing chains of the individual contributions. Due to the long observation period, the model consists not only of a static gravity field, but comprises additionally modeled temporal variations. These are represented by time variable spherical harmonic coefficients, using a deterministic model for a regularized trend and annual oscillation. The main focus within the GOCO combination process is on the proper handling of the stochastic behavior of the input data. Appropriate noise modelling for the used observations result in realistic accuracy information for the derived gravity field solution. This accuracy information, represented by the full variance-covariance matrix, is extremely useful for further combination with, for example, terrestrial gravity data and is published together with the solution. The primary model data (Kvas et al., 2019b) consisting of potential coefficients representing Earth's static gravity field, together with secular and annual variations are available on International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/, last accessed 2020-06-11). This data set is identified with the DOI https://doi.org/10.5880/ICGEM.2019.002. Supplementary material consisting of the full variance-covariance matrix of the static potential coefficients and estimated co-seismic mass changes are available on https://ifg.tugraz.at/GOCO (last accessed 2020-06-11).

scholarly journals GOCO06s – a satellite-only global gravity field model

2021 ◽  
Vol 13 (1) ◽  
pp. 99-118
Author(s):  
Andreas Kvas ◽  
Jan Martin Brockmann ◽  
Sandro Krauss ◽  
Till Schubert ◽  
Thomas Gruber ◽  
...  
Keyword(s):  
Gravity Field ◽  
Covariance Matrix ◽  
Field Model ◽  
Data Set ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Gravity Observation ◽  
Primary Model ◽  
Global Gravity Field Model ◽  
Variance Covariance Matrix

Abstract. GOCO06s is the latest satellite-only global gravity field model computed by the GOCO (Gravity Observation Combination) project. It is based on over a billion observations acquired over 15 years from 19 satellites with different complementary observation principles. This combination of different measurement techniques is key in providing consistently high accuracy and best possible spatial resolution of the Earth's gravity field. The motivation for the new release was the availability of reprocessed observation data for the Gravity Recovery and Climate Experiment (GRACE) and Gravity field and steady-state Ocean Circulation Explorer (GOCE), updated background models, and substantial improvements in the processing chains of the individual contributions. Due to the long observation period, the model consists not only of a static gravity field, but comprises additionally modeled temporal variations. These are represented by time-variable spherical harmonic coefficients, using a deterministic model for a regularized trend and annual oscillation. The main focus within the GOCO combination process is on the proper handling of the stochastic behavior of the input data. Appropriate noise modeling for the observations used results in realistic accuracy information for the derived gravity field solution. This accuracy information, represented by the full variance–covariance matrix, is extremely useful for further combination with, for example, terrestrial gravity data and is published together with the solution. The primary model data consisting of potential coefficients representing Earth's static gravity field, together with secular and annual variations, are available on the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/, last access: 11 June 2020). This data set is identified with the following DOI: https://doi.org/10.5880/ICGEM.2019.002 (Kvas et al., 2019b). Supplementary material consisting of the full variance–covariance matrix of the static potential coefficients and estimated co-seismic mass changes is available at https://ifg.tugraz.at/GOCO (last access: 11 June 2020).

scholarly journals Mass distribution of Earth landforms determined by aspects of the geopotential as computed from the global gravity field model EGM 2008

2013 ◽  
Vol 48 (2) ◽  
pp. 17-25 ◽  
Author(s):  
Jan Kalvoda ◽  
Jaroslav Klokočník ◽  
Jan Kostelecký ◽  
Aleš Bezděk
Keyword(s):  
Gravity Field ◽  
Mass Distribution ◽  
Field Model ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Global Gravity Field Model
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